4,140 research outputs found
Inferring Narrative Causality between Event Pairs in Films
To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams
Improving Topic Segmentation by Injecting Discourse Dependencies
Recent neural supervised topic segmentation models achieve distinguished
superior effectiveness over unsupervised methods, with the availability of
large-scale training corpora sampled from Wikipedia. These models may, however,
suffer from limited robustness and transferability caused by exploiting simple
linguistic cues for prediction, but overlooking more important inter-sentential
topical consistency. To address this issue, we present a discourse-aware neural
topic segmentation model with the injection of above-sentence discourse
dependency structures to encourage the model make topic boundary prediction
based more on the topical consistency between sentences. Our empirical study on
English evaluation datasets shows that injecting above-sentence discourse
structures to a neural topic segmenter with our proposed strategy can
substantially improve its performances on intra-domain and out-of-domain data,
with little increase of model's complexity.Comment: Accepted to the 3rd Workshop on Computational Approaches to Discourse
(CODI-2022) at COLING 202
What you say and how you say it : joint modeling of topics and discourse in microblog conversations
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier
- …